Energy overview of Botswana: generation and consumption

Botswana has a basic need to explore its energy concept, this being its energy sources, generation and percentage of the population with access to electricity. At present, Botswana generates electricity from coal, which supplies about 29% (on average) of the country’s demand. The other 71% is imported mainly from South Africa (Eskom). Consequently, the dependence of Botswana on imports posses threats to the security of its energy supply. As a result, there is the need to understand the bases for a possible generation expansion that would substantiate existing documentation. In view of this need, this study investigates the existing energy sources as well as energy consumption and production levels in Botswana. The study would be further developed by making projections of the energy demand up until the year 2020. The key techniques that were used include; literature review, questionnaire survey and an empirical study. The results presented indicated that, current dependable operation capacity (i.e. 100MW) should be increased to 2,595 MW or more assuming 85% plant efficiency. This would then be able to meet the growing demand for energy use. In addition, the installed capacity would be able to support commercial and mining activities for the growth of the economy.

An overview of the use of neural networks for data mining tasks

In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks. © 2012 Wiley Periodicals, Inc.

Corporate governance, accountability and mechanisms of accountability: an overview

Purpose – This paper aims to review traditional corporate governance and accountability research, to suggest opportunities for future research in this field. Design/methodology/approach – The first part adopts an analytical frame of reference based on theory, accountability mechanisms, methodology, business sector/context, globalisation and time horizon. The second part of the paper locates the seven papers in the special issue in a framework of analysis showing how each one contributes to the field. The paper presents a frame of reference which may be used as a “roadmap” for researchers to navigate their way through the prior literature and to position their work on the frontiers of corporate governance research. The paper is primarily discursive and conceptual. Findings – The paper encourages broader approaches to corporate governance and accountability research beyond the traditional and primarily quantitative approaches of prior research. Broader theoretical perspectives, methodological approaches, accountability mechanism, sectors/contexts, globalisation, and time horizons are identified. Research limitations/implications – Greater use of qualitative research methods are suggested, which present challenges particularly of access to the “black box” of corporate boardrooms. Originality/value – Drawing on the analytical framework, and the papers in the special issue, the paper identifies opportunities for further research of accountability and corporate governance. Keywords Corporate governance, Management accountability, Research Paper type General review

Highly resolved observations of trace gases in the lowermost stratosphere and upper troposphere from the SPURT project: an overview

During SPURT (Spurenstofftransport in der Tropopausenregion, trace gas transport in the tropopause region) we performed measurements of a wide range of trace gases with different lifetimes and sink/source characteristics in the northern hemispheric upper troposphere (UT) and lowermost stratosphere (LMS). A large number of in-situ instruments were deployed on board a Learjet 35A, flying at altitudes up to 13.7 km, at times reaching to nearly 380 K potential temperature. Eight measurement campaigns (consisting of a total of 36 flights), distributed over all seasons and typically covering latitudes between 35° N and 75° N in the European longitude sector (10° W–20° E), were performed. Here we present an overview of the project, describing the instrumentation, the encountered meteorological situations during the campaigns and the data set available from SPURT. Measurements were obtained for N2O, CH4, CO, CO2, CFC12, H2, SF6, NO, NOy, O3 and H2O. We illustrate the strength of this new data set by showing mean distributions of the mixing ratios of selected trace gases, using a potential temperature-equivalent latitude coordinate system. The observations reveal that the LMS is most stratospheric in character during spring, with the highest mixing ratios of O3 and NOy and the lowest mixing ratios of N2O and SF6. The lowest mixing ratios of NOy and O3 are observed during autumn, together with the highest mixing ratios of N2O and SF6 indicating a strong tropospheric influence. For H2O, however, the maximum concentrations in the LMS are found during summer, suggesting unique (temperature- and convection-controlled) conditions for this molecule during transport across the tropopause. The SPURT data set is presently the most accurate and complete data set for many trace species in the LMS, and its main value is the simultaneous measurement of a suite of trace gases having different lifetimes and physical-chemical histories. It is thus very well suited for studies of atmospheric transport, for model validation, and for investigations of seasonal changes in the UT/LMS, as demonstrated in accompanying and elsewhere published studies.

An overview of interactive visual data mining techniques for knowledge discovery

n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.

An overview of mixed-effects statistical models for second language researchers

As in any field of scientific inquiry, advancements in the field of second language acquisition (SLA) rely in part on the interpretation and generalizability of study findings using quantitative data analysis and inferential statistics. While statistical techniques such as ANOVA and t-tests are widely used in second language research, this review article provides a review of a class of newer statistical models that have not yet been widely adopted in the field, but have garnered interest in other fields of language research. The class of statistical models called mixed-effects models are introduced, and the potential benefits of these models for the second language researcher are discussed. A simple example of mixed-effects data analysis using the statistical software package R (R Development Core Team, 2011) is provided as an introduction to the use of these statistical techniques, and to exemplify how such analyses can be reported in research articles. It is concluded that mixed-effects models provide the second language researcher with a powerful tool for the analysis of a variety of types of second language acquisition data.

‘Low carbon’ scenarios, roadmaps, transitions and pathways: an overview and discussion

There are a range of studies based in the low carbon arena which use various ‘futures’- based techniques as ways of exploring uncertainties. These techniques range from ‘scenarios’ and ‘roadmaps’ through to ‘transitions’ and ‘pathways’ as well as ‘vision’-based techniques. The overall aim of the paper is therefore to compare and contrast these techniques to develop a simple working typology with the further objective of identifying the implications of this analysis for RETROFIT 2050. Using recent examples of city-based and energy-based studies throughout, the paper compares and contrasts these techniques and finds that the distinctions between them have often been blurred in the field of low carbon. Visions, for example, have been used in both transition theory and futures/Foresight methods, and scenarios have also been used in transition-based studies as well as futures/Foresight studies. Moreover, Foresight techniques which capture expert knowledge and map existing knowledge to develop a set of scenarios and roadmaps which can inform the development of transitions and pathways can not only help potentially overcome any ‘disconnections’ that may exist between the social and the technical lenses in which such future trajectories are mapped, but also promote a strong ‘co-evolutionary’ content.